R version 2.9.0 (2009-04-17)
Copyright (C) 2009 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
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> x <- array(list(2.6,30.5,2.4,28.6,2.5,30,2.7,28.2,3.2,27.6,2.8,24.9,2.8,23.8,3,24.3,3.1,23.6,3.1,24.2,3,28.1,2.4,30.1,2.7,31.1,3,32,2.7,32.4,2.7,34,2,35.1,2.4,37.1,2.6,37.3,2.4,38.1,2.3,39.5,2.4,38.3,2.5,37.3,2.6,38.7,2.6,37.5,2.6,38.7,2.7,37.9,2.8,36.6,2.6,35.5,2.6,37.6,2,38.6,2,40.3,2.1,39,1.9,36.8,2,36.5,2.5,34.1,2.9,34.2,3.3,31.9,3.5,33.7,3.8,33.5,4.6,33.8,4.4,29.9,5.3,32.3,5.8,30.5,5.9,28.5,5.6,29,5.8,23.8,5.5,17.9,4.6,9.9,4.2,3,4,4.2,3.5,0.4,2.3,0,2.2,2.4,1.4,4.2,0.6,8.2,0,9,0.5,13.6,0.1,14,0.1,17.6),dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> y <- array(NA,dim=c(2,60),dimnames=list(c('Y','X'),1:60))
> for (i in 1:dim(x)[1])
+ {
+ for (j in 1:dim(x)[2])
+ {
+ y[i,j] <- as.numeric(x[i,j])
+ }
+ }
> par3 = 'No Linear Trend'
> par2 = 'Include Monthly Dummies'
> par1 = '1'
> #'GNU S' R Code compiled by R2WASP v. 1.0.44 ()
> #Author: Prof. Dr. P. Wessa
> #To cite this work: AUTHOR(S), (YEAR), YOUR SOFTWARE TITLE (vNUMBER) in Free Statistics Software (v$_version), Office for Research Development and Education, URL http://www.wessa.net/rwasp_YOURPAGE.wasp/
> #Source of accompanying publication: Office for Research, Development, and Education
> #Technical description: Write here your technical program description (don't use hard returns!)
> library(lattice)
> library(lmtest)
Loading required package: zoo
Attaching package: 'zoo'
The following object(s) are masked from package:base :
as.Date.numeric
> n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
> par1 <- as.numeric(par1)
> x <- t(y)
> k <- length(x[1,])
> n <- length(x[,1])
> x1 <- cbind(x[,par1], x[,1:k!=par1])
> mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
> colnames(x1) <- mycolnames #colnames(x)[par1]
> x <- x1
> if (par3 == 'First Differences'){
+ x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
+ for (i in 1:n-1) {
+ for (j in 1:k) {
+ x2[i,j] <- x[i+1,j] - x[i,j]
+ }
+ }
+ x <- x2
+ }
> if (par2 == 'Include Monthly Dummies'){
+ x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
+ for (i in 1:11){
+ x2[seq(i,n,12),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> if (par2 == 'Include Quarterly Dummies'){
+ x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
+ for (i in 1:3){
+ x2[seq(i,n,4),i] <- 1
+ }
+ x <- cbind(x, x2)
+ }
> k <- length(x[1,])
> if (par3 == 'Linear Trend'){
+ x <- cbind(x, c(1:n))
+ colnames(x)[k+1] <- 't'
+ }
> x
Y X M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11
1 2.6 30.5 1 0 0 0 0 0 0 0 0 0 0
2 2.4 28.6 0 1 0 0 0 0 0 0 0 0 0
3 2.5 30.0 0 0 1 0 0 0 0 0 0 0 0
4 2.7 28.2 0 0 0 1 0 0 0 0 0 0 0
5 3.2 27.6 0 0 0 0 1 0 0 0 0 0 0
6 2.8 24.9 0 0 0 0 0 1 0 0 0 0 0
7 2.8 23.8 0 0 0 0 0 0 1 0 0 0 0
8 3.0 24.3 0 0 0 0 0 0 0 1 0 0 0
9 3.1 23.6 0 0 0 0 0 0 0 0 1 0 0
10 3.1 24.2 0 0 0 0 0 0 0 0 0 1 0
11 3.0 28.1 0 0 0 0 0 0 0 0 0 0 1
12 2.4 30.1 0 0 0 0 0 0 0 0 0 0 0
13 2.7 31.1 1 0 0 0 0 0 0 0 0 0 0
14 3.0 32.0 0 1 0 0 0 0 0 0 0 0 0
15 2.7 32.4 0 0 1 0 0 0 0 0 0 0 0
16 2.7 34.0 0 0 0 1 0 0 0 0 0 0 0
17 2.0 35.1 0 0 0 0 1 0 0 0 0 0 0
18 2.4 37.1 0 0 0 0 0 1 0 0 0 0 0
19 2.6 37.3 0 0 0 0 0 0 1 0 0 0 0
20 2.4 38.1 0 0 0 0 0 0 0 1 0 0 0
21 2.3 39.5 0 0 0 0 0 0 0 0 1 0 0
22 2.4 38.3 0 0 0 0 0 0 0 0 0 1 0
23 2.5 37.3 0 0 0 0 0 0 0 0 0 0 1
24 2.6 38.7 0 0 0 0 0 0 0 0 0 0 0
25 2.6 37.5 1 0 0 0 0 0 0 0 0 0 0
26 2.6 38.7 0 1 0 0 0 0 0 0 0 0 0
27 2.7 37.9 0 0 1 0 0 0 0 0 0 0 0
28 2.8 36.6 0 0 0 1 0 0 0 0 0 0 0
29 2.6 35.5 0 0 0 0 1 0 0 0 0 0 0
30 2.6 37.6 0 0 0 0 0 1 0 0 0 0 0
31 2.0 38.6 0 0 0 0 0 0 1 0 0 0 0
32 2.0 40.3 0 0 0 0 0 0 0 1 0 0 0
33 2.1 39.0 0 0 0 0 0 0 0 0 1 0 0
34 1.9 36.8 0 0 0 0 0 0 0 0 0 1 0
35 2.0 36.5 0 0 0 0 0 0 0 0 0 0 1
36 2.5 34.1 0 0 0 0 0 0 0 0 0 0 0
37 2.9 34.2 1 0 0 0 0 0 0 0 0 0 0
38 3.3 31.9 0 1 0 0 0 0 0 0 0 0 0
39 3.5 33.7 0 0 1 0 0 0 0 0 0 0 0
40 3.8 33.5 0 0 0 1 0 0 0 0 0 0 0
41 4.6 33.8 0 0 0 0 1 0 0 0 0 0 0
42 4.4 29.9 0 0 0 0 0 1 0 0 0 0 0
43 5.3 32.3 0 0 0 0 0 0 1 0 0 0 0
44 5.8 30.5 0 0 0 0 0 0 0 1 0 0 0
45 5.9 28.5 0 0 0 0 0 0 0 0 1 0 0
46 5.6 29.0 0 0 0 0 0 0 0 0 0 1 0
47 5.8 23.8 0 0 0 0 0 0 0 0 0 0 1
48 5.5 17.9 0 0 0 0 0 0 0 0 0 0 0
49 4.6 9.9 1 0 0 0 0 0 0 0 0 0 0
50 4.2 3.0 0 1 0 0 0 0 0 0 0 0 0
51 4.0 4.2 0 0 1 0 0 0 0 0 0 0 0
52 3.5 0.4 0 0 0 1 0 0 0 0 0 0 0
53 2.3 0.0 0 0 0 0 1 0 0 0 0 0 0
54 2.2 2.4 0 0 0 0 0 1 0 0 0 0 0
55 1.4 4.2 0 0 0 0 0 0 1 0 0 0 0
56 0.6 8.2 0 0 0 0 0 0 0 1 0 0 0
57 0.0 9.0 0 0 0 0 0 0 0 0 1 0 0
58 0.5 13.6 0 0 0 0 0 0 0 0 0 1 0
59 0.1 14.0 0 0 0 0 0 0 0 0 0 0 1
60 0.1 17.6 0 0 0 0 0 0 0 0 0 0 0
> k <- length(x[1,])
> df <- as.data.frame(x)
> (mylm <- lm(df))
Call:
lm(formula = df)
Coefficients:
(Intercept) X M1 M2 M3 M4
2.374701 0.008862 0.451493 0.487444 0.460354 0.490103
M5 M6 M7 M8 M9 M10
0.331343 0.271521 0.203899 0.134683 0.057873 0.073797
M11
0.057696
> (mysum <- summary(mylm))
Call:
lm(formula = df)
Residuals:
Min 1Q Median 3Q Max
-2.5123 -0.5815 -0.3837 0.3840 3.2149
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.374701 0.803879 2.954 0.00489 **
X 0.008862 0.016647 0.532 0.59700
M1 0.451493 0.931689 0.485 0.63021
M2 0.487444 0.931657 0.523 0.60329
M3 0.460354 0.931552 0.494 0.62348
M4 0.490103 0.931745 0.526 0.60136
M5 0.331343 0.931796 0.356 0.72373
M6 0.271521 0.931803 0.291 0.77203
M7 0.203899 0.931581 0.219 0.82770
M8 0.134683 0.931605 0.145 0.88567
M9 0.057873 0.931560 0.062 0.95073
M10 0.073797 0.931625 0.079 0.93720
M11 0.057696 0.931562 0.062 0.95088
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.473 on 47 degrees of freedom
Multiple R-squared: 0.02394, Adjusted R-squared: -0.2253
F-statistic: 0.09606 on 12 and 47 DF, p-value: 1
> if (n > n25) {
+ kp3 <- k + 3
+ nmkm3 <- n - k - 3
+ gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
+ numgqtests <- 0
+ numsignificant1 <- 0
+ numsignificant5 <- 0
+ numsignificant10 <- 0
+ for (mypoint in kp3:nmkm3) {
+ j <- 0
+ numgqtests <- numgqtests + 1
+ for (myalt in c('greater', 'two.sided', 'less')) {
+ j <- j + 1
+ gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
+ }
+ if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
+ if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
+ }
+ gqarr
+ }
[,1] [,2] [,3]
[1,] 2.505299e-03 5.010598e-03 0.9974947
[2,] 6.340614e-03 1.268123e-02 0.9936594
[3,] 1.360373e-03 2.720747e-03 0.9986396
[4,] 2.938182e-04 5.876364e-04 0.9997062
[5,] 5.262595e-05 1.052519e-04 0.9999474
[6,] 9.289055e-06 1.857811e-05 0.9999907
[7,] 1.458368e-06 2.916737e-06 0.9999985
[8,] 2.130175e-07 4.260349e-07 0.9999998
[9,] 6.778133e-08 1.355627e-07 0.9999999
[10,] 1.163536e-08 2.327073e-08 1.0000000
[11,] 2.006944e-09 4.013888e-09 1.0000000
[12,] 4.431652e-10 8.863303e-10 1.0000000
[13,] 8.417889e-11 1.683578e-10 1.0000000
[14,] 1.222241e-11 2.444482e-11 1.0000000
[15,] 1.931548e-12 3.863095e-12 1.0000000
[16,] 7.277373e-13 1.455475e-12 1.0000000
[17,] 2.244792e-13 4.489585e-13 1.0000000
[18,] 5.391372e-14 1.078274e-13 1.0000000
[19,] 4.483288e-14 8.966576e-14 1.0000000
[20,] 2.884186e-14 5.768371e-14 1.0000000
[21,] 4.101944e-15 8.203887e-15 1.0000000
[22,] 2.137892e-15 4.275785e-15 1.0000000
[23,] 3.420607e-15 6.841214e-15 1.0000000
[24,] 3.197981e-14 6.395963e-14 1.0000000
[25,] 1.498490e-12 2.996979e-12 1.0000000
[26,] 2.357040e-09 4.714080e-09 1.0000000
[27,] 1.056206e-07 2.112413e-07 0.9999999
[28,] 3.857837e-05 7.715674e-05 0.9999614
[29,] 4.622034e-04 9.244067e-04 0.9995378
> postscript(file="/var/www/html/rcomp/tmp/1osz41261396196.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
> points(x[,1]-mysum$resid)
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/2qgb11261396196.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/3m49g1261396196.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/4ilun1261396196.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/5ou3v1261396196.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
> qqline(mysum$resid)
> grid()
> dev.off()
null device
1
> (myerror <- as.ts(mysum$resid))
Time Series:
Start = 1
End = 60
Frequency = 1
1 2 3 4 5 6
-0.49648321 -0.71559702 -0.60091418 -0.41471082 0.24936567 -0.06688433
7 8 9 10 11 12
0.01048508 0.27527053 0.45828359 0.43704292 0.31858209 -0.24144590
13 14 15 16 17 18
-0.40180038 -0.14572762 -0.42218285 -0.46611009 -1.01709890 -0.57500002
19 20 21 22 23 24
-0.30915114 -0.44702427 -0.48262129 -0.38791047 -0.26294778 -0.11765861
25 26 27 28 29 30
-0.55851681 -0.60510264 -0.47092353 -0.38915114 -0.42064368 -0.37943099
31 32 33 34 35 36
-0.92067167 -0.86652055 -0.67819032 -0.87461756 -0.75585823 -0.17689367
37 38 39 40 41 42
-0.22927240 0.15515857 0.36629663 0.63832088 1.59442163 1.48880596
43 44 45 46 47 48
2.43515857 3.02032649 3.21486007 2.89450560 3.15668844 2.96666980
49 50 51 52 53 54
1.68607280 1.31126871 1.12772393 0.63165118 -0.40604472 -0.46749062
55 56 57 58 59 60
-1.21582085 -1.98205220 -2.51233205 -2.06902049 -2.45646452 -2.43067162
> postscript(file="/var/www/html/rcomp/tmp/6mx211261396196.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> dum <- cbind(lag(myerror,k=1),myerror)
> dum
Time Series:
Start = 0
End = 60
Frequency = 1
lag(myerror, k = 1) myerror
0 -0.49648321 NA
1 -0.71559702 -0.49648321
2 -0.60091418 -0.71559702
3 -0.41471082 -0.60091418
4 0.24936567 -0.41471082
5 -0.06688433 0.24936567
6 0.01048508 -0.06688433
7 0.27527053 0.01048508
8 0.45828359 0.27527053
9 0.43704292 0.45828359
10 0.31858209 0.43704292
11 -0.24144590 0.31858209
12 -0.40180038 -0.24144590
13 -0.14572762 -0.40180038
14 -0.42218285 -0.14572762
15 -0.46611009 -0.42218285
16 -1.01709890 -0.46611009
17 -0.57500002 -1.01709890
18 -0.30915114 -0.57500002
19 -0.44702427 -0.30915114
20 -0.48262129 -0.44702427
21 -0.38791047 -0.48262129
22 -0.26294778 -0.38791047
23 -0.11765861 -0.26294778
24 -0.55851681 -0.11765861
25 -0.60510264 -0.55851681
26 -0.47092353 -0.60510264
27 -0.38915114 -0.47092353
28 -0.42064368 -0.38915114
29 -0.37943099 -0.42064368
30 -0.92067167 -0.37943099
31 -0.86652055 -0.92067167
32 -0.67819032 -0.86652055
33 -0.87461756 -0.67819032
34 -0.75585823 -0.87461756
35 -0.17689367 -0.75585823
36 -0.22927240 -0.17689367
37 0.15515857 -0.22927240
38 0.36629663 0.15515857
39 0.63832088 0.36629663
40 1.59442163 0.63832088
41 1.48880596 1.59442163
42 2.43515857 1.48880596
43 3.02032649 2.43515857
44 3.21486007 3.02032649
45 2.89450560 3.21486007
46 3.15668844 2.89450560
47 2.96666980 3.15668844
48 1.68607280 2.96666980
49 1.31126871 1.68607280
50 1.12772393 1.31126871
51 0.63165118 1.12772393
52 -0.40604472 0.63165118
53 -0.46749062 -0.40604472
54 -1.21582085 -0.46749062
55 -1.98205220 -1.21582085
56 -2.51233205 -1.98205220
57 -2.06902049 -2.51233205
58 -2.45646452 -2.06902049
59 -2.43067162 -2.45646452
60 NA -2.43067162
> dum1 <- dum[2:length(myerror),]
> dum1
lag(myerror, k = 1) myerror
[1,] -0.71559702 -0.49648321
[2,] -0.60091418 -0.71559702
[3,] -0.41471082 -0.60091418
[4,] 0.24936567 -0.41471082
[5,] -0.06688433 0.24936567
[6,] 0.01048508 -0.06688433
[7,] 0.27527053 0.01048508
[8,] 0.45828359 0.27527053
[9,] 0.43704292 0.45828359
[10,] 0.31858209 0.43704292
[11,] -0.24144590 0.31858209
[12,] -0.40180038 -0.24144590
[13,] -0.14572762 -0.40180038
[14,] -0.42218285 -0.14572762
[15,] -0.46611009 -0.42218285
[16,] -1.01709890 -0.46611009
[17,] -0.57500002 -1.01709890
[18,] -0.30915114 -0.57500002
[19,] -0.44702427 -0.30915114
[20,] -0.48262129 -0.44702427
[21,] -0.38791047 -0.48262129
[22,] -0.26294778 -0.38791047
[23,] -0.11765861 -0.26294778
[24,] -0.55851681 -0.11765861
[25,] -0.60510264 -0.55851681
[26,] -0.47092353 -0.60510264
[27,] -0.38915114 -0.47092353
[28,] -0.42064368 -0.38915114
[29,] -0.37943099 -0.42064368
[30,] -0.92067167 -0.37943099
[31,] -0.86652055 -0.92067167
[32,] -0.67819032 -0.86652055
[33,] -0.87461756 -0.67819032
[34,] -0.75585823 -0.87461756
[35,] -0.17689367 -0.75585823
[36,] -0.22927240 -0.17689367
[37,] 0.15515857 -0.22927240
[38,] 0.36629663 0.15515857
[39,] 0.63832088 0.36629663
[40,] 1.59442163 0.63832088
[41,] 1.48880596 1.59442163
[42,] 2.43515857 1.48880596
[43,] 3.02032649 2.43515857
[44,] 3.21486007 3.02032649
[45,] 2.89450560 3.21486007
[46,] 3.15668844 2.89450560
[47,] 2.96666980 3.15668844
[48,] 1.68607280 2.96666980
[49,] 1.31126871 1.68607280
[50,] 1.12772393 1.31126871
[51,] 0.63165118 1.12772393
[52,] -0.40604472 0.63165118
[53,] -0.46749062 -0.40604472
[54,] -1.21582085 -0.46749062
[55,] -1.98205220 -1.21582085
[56,] -2.51233205 -1.98205220
[57,] -2.06902049 -2.51233205
[58,] -2.45646452 -2.06902049
[59,] -2.43067162 -2.45646452
> z <- as.data.frame(dum1)
> z
lag(myerror, k = 1) myerror
1 -0.71559702 -0.49648321
2 -0.60091418 -0.71559702
3 -0.41471082 -0.60091418
4 0.24936567 -0.41471082
5 -0.06688433 0.24936567
6 0.01048508 -0.06688433
7 0.27527053 0.01048508
8 0.45828359 0.27527053
9 0.43704292 0.45828359
10 0.31858209 0.43704292
11 -0.24144590 0.31858209
12 -0.40180038 -0.24144590
13 -0.14572762 -0.40180038
14 -0.42218285 -0.14572762
15 -0.46611009 -0.42218285
16 -1.01709890 -0.46611009
17 -0.57500002 -1.01709890
18 -0.30915114 -0.57500002
19 -0.44702427 -0.30915114
20 -0.48262129 -0.44702427
21 -0.38791047 -0.48262129
22 -0.26294778 -0.38791047
23 -0.11765861 -0.26294778
24 -0.55851681 -0.11765861
25 -0.60510264 -0.55851681
26 -0.47092353 -0.60510264
27 -0.38915114 -0.47092353
28 -0.42064368 -0.38915114
29 -0.37943099 -0.42064368
30 -0.92067167 -0.37943099
31 -0.86652055 -0.92067167
32 -0.67819032 -0.86652055
33 -0.87461756 -0.67819032
34 -0.75585823 -0.87461756
35 -0.17689367 -0.75585823
36 -0.22927240 -0.17689367
37 0.15515857 -0.22927240
38 0.36629663 0.15515857
39 0.63832088 0.36629663
40 1.59442163 0.63832088
41 1.48880596 1.59442163
42 2.43515857 1.48880596
43 3.02032649 2.43515857
44 3.21486007 3.02032649
45 2.89450560 3.21486007
46 3.15668844 2.89450560
47 2.96666980 3.15668844
48 1.68607280 2.96666980
49 1.31126871 1.68607280
50 1.12772393 1.31126871
51 0.63165118 1.12772393
52 -0.40604472 0.63165118
53 -0.46749062 -0.40604472
54 -1.21582085 -0.46749062
55 -1.98205220 -1.21582085
56 -2.51233205 -1.98205220
57 -2.06902049 -2.51233205
58 -2.45646452 -2.06902049
59 -2.43067162 -2.45646452
> plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
> lines(lowess(z))
> abline(lm(z))
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/7r7nj1261396196.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/8b92h1261396196.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
> grid()
> dev.off()
null device
1
> postscript(file="/var/www/html/rcomp/tmp/9shpo1261396196.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
> opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
> plot(mylm, las = 1, sub='Residual Diagnostics')
> par(opar)
> dev.off()
null device
1
> if (n > n25) {
+ postscript(file="/var/www/html/rcomp/tmp/10wxo91261396196.ps",horizontal=F,pagecentre=F,paper="special",width=8.3333333333333,height=5.5555555555556)
+ plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
+ grid()
+ dev.off()
+ }
null device
1
>
> #Note: the /var/www/html/rcomp/createtable file can be downloaded at http://www.wessa.net/cretab
> load(file="/var/www/html/rcomp/createtable")
>
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
> a<-table.row.end(a)
> myeq <- colnames(x)[1]
> myeq <- paste(myeq, '[t] = ', sep='')
> for (i in 1:k){
+ if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
+ myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
+ if (rownames(mysum$coefficients)[i] != '(Intercept)') {
+ myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
+ if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
+ }
+ }
> myeq <- paste(myeq, ' + e[t]')
> a<-table.row.start(a)
> a<-table.element(a, myeq)
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/119otr1261396196.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a,hyperlink('http://www.xycoon.com/ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a,'Variable',header=TRUE)
> a<-table.element(a,'Parameter',header=TRUE)
> a<-table.element(a,'S.D.',header=TRUE)
> a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
> a<-table.element(a,'2-tail p-value',header=TRUE)
> a<-table.element(a,'1-tail p-value',header=TRUE)
> a<-table.row.end(a)
> for (i in 1:k){
+ a<-table.row.start(a)
+ a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
+ a<-table.element(a,mysum$coefficients[i,1])
+ a<-table.element(a, round(mysum$coefficients[i,2],6))
+ a<-table.element(a, round(mysum$coefficients[i,3],4))
+ a<-table.element(a, round(mysum$coefficients[i,4],6))
+ a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/126z8s1261396196.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple R',1,TRUE)
> a<-table.element(a, sqrt(mysum$r.squared))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'R-squared',1,TRUE)
> a<-table.element(a, mysum$r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Adjusted R-squared',1,TRUE)
> a<-table.element(a, mysum$adj.r.squared)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (value)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[1])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[2])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
> a<-table.element(a, mysum$fstatistic[3])
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'p-value',1,TRUE)
> a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
> a<-table.element(a, mysum$sigma)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
> a<-table.element(a, sum(myerror*myerror))
> a<-table.row.end(a)
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/13kt941261396196.tab")
> a<-table.start()
> a<-table.row.start(a)
> a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
> a<-table.row.end(a)
> a<-table.row.start(a)
> a<-table.element(a, 'Time or Index', 1, TRUE)
> a<-table.element(a, 'Actuals', 1, TRUE)
> a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
> a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
> a<-table.row.end(a)
> for (i in 1:n) {
+ a<-table.row.start(a)
+ a<-table.element(a,i, 1, TRUE)
+ a<-table.element(a,x[i])
+ a<-table.element(a,x[i]-mysum$resid[i])
+ a<-table.element(a,mysum$resid[i])
+ a<-table.row.end(a)
+ }
> a<-table.end(a)
> table.save(a,file="/var/www/html/rcomp/tmp/147h6l1261396196.tab")
> if (n > n25) {
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'p-values',header=TRUE)
+ a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'breakpoint index',header=TRUE)
+ a<-table.element(a,'greater',header=TRUE)
+ a<-table.element(a,'2-sided',header=TRUE)
+ a<-table.element(a,'less',header=TRUE)
+ a<-table.row.end(a)
+ for (mypoint in kp3:nmkm3) {
+ a<-table.row.start(a)
+ a<-table.element(a,mypoint,header=TRUE)
+ a<-table.element(a,gqarr[mypoint-kp3+1,1])
+ a<-table.element(a,gqarr[mypoint-kp3+1,2])
+ a<-table.element(a,gqarr[mypoint-kp3+1,3])
+ a<-table.row.end(a)
+ }
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/15lofw1261396196.tab")
+ a<-table.start()
+ a<-table.row.start(a)
+ a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'Description',header=TRUE)
+ a<-table.element(a,'# significant tests',header=TRUE)
+ a<-table.element(a,'% significant tests',header=TRUE)
+ a<-table.element(a,'OK/NOK',header=TRUE)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'1% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant1)
+ a<-table.element(a,numsignificant1/numgqtests)
+ if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'5% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant5)
+ a<-table.element(a,numsignificant5/numgqtests)
+ if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.row.start(a)
+ a<-table.element(a,'10% type I error level',header=TRUE)
+ a<-table.element(a,numsignificant10)
+ a<-table.element(a,numsignificant10/numgqtests)
+ if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
+ a<-table.element(a,dum)
+ a<-table.row.end(a)
+ a<-table.end(a)
+ table.save(a,file="/var/www/html/rcomp/tmp/163chd1261396196.tab")
+ }
>
> try(system("convert tmp/1osz41261396196.ps tmp/1osz41261396196.png",intern=TRUE))
character(0)
> try(system("convert tmp/2qgb11261396196.ps tmp/2qgb11261396196.png",intern=TRUE))
character(0)
> try(system("convert tmp/3m49g1261396196.ps tmp/3m49g1261396196.png",intern=TRUE))
character(0)
> try(system("convert tmp/4ilun1261396196.ps tmp/4ilun1261396196.png",intern=TRUE))
character(0)
> try(system("convert tmp/5ou3v1261396196.ps tmp/5ou3v1261396196.png",intern=TRUE))
character(0)
> try(system("convert tmp/6mx211261396196.ps tmp/6mx211261396196.png",intern=TRUE))
character(0)
> try(system("convert tmp/7r7nj1261396196.ps tmp/7r7nj1261396196.png",intern=TRUE))
character(0)
> try(system("convert tmp/8b92h1261396196.ps tmp/8b92h1261396196.png",intern=TRUE))
character(0)
> try(system("convert tmp/9shpo1261396196.ps tmp/9shpo1261396196.png",intern=TRUE))
character(0)
> try(system("convert tmp/10wxo91261396196.ps tmp/10wxo91261396196.png",intern=TRUE))
character(0)
>
>
> proc.time()
user system elapsed
2.377 1.573 2.996